qnguyen3 / chat-with-mlx

An all-in-one LLMs Chat UI for Apple Silicon Mac using MLX Framework.
https://twitter.com/stablequan
MIT License
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⬆️ Bump gradio from 4.29.0 to 4.38.1 #201

Closed dependabot[bot] closed 4 months ago

dependabot[bot] commented 4 months ago

Bumps gradio from 4.29.0 to 4.38.1.

Release notes

Sourced from gradio's releases.

gradio@4.38.1

Features

@​gradio/lite@​4.38.1

Dependency updates

  • gradio@4.38.1

gradio@4.38.0

Highlights

Support message format in chatbot 💬 (#8422 4221290)

gr.Chatbot and gr.ChatInterface now support the Messages API, which is fully compatible with LLM API providers such as Hugging Face Text Generation Inference, OpenAI's chat completions API, and Llama.cpp server.

Building Gradio applications around these LLM solutions is now even easier!

gr.Chatbot and gr.ChatInterface now have a type parameter that can accept two values - 'tuples' and 'messages'. If set to 'tuples', the default chatbot data format is expected. If set to 'messages', a list of dictionaries with content and role keys is expected. See below -

def chat_greeter(msg, history):
    history.append({"role": "assistant", "content": "Hello!"})
    return history

Additionally, gradio now exposes a gr.ChatMessage dataclass you can use for IDE type hints and auto completion.

Tool use in Chatbot 🛠️

The Gradio Chatbot can now natively display tool usage and intermediate thoughts common in Agent and chain-of-thought workflows!

If you are using the new "messages" format, simply add a metadata key with a dictionary containing a title key and value. This will display the assistant message in an expandable message box to show the result of a tool or intermediate step.

import gradio as gr
from gradio import ChatMessage
import time

def generate_response(history):
history.append(ChatMessage(role="user", content="What is the weather in San Francisco right now?"))
yield history
time.sleep(0.25)
history.append(ChatMessage(role="assistant",
content="In order to find the current weather in San Francisco, I will need to use my weather tool.")
)
yield history
</tr></table>

... (truncated)

Changelog

Sourced from gradio's changelog.

4.38.1

Features

4.38.0

Highlights

Support message format in chatbot 💬 (#8422 4221290)

gr.Chatbot and gr.ChatInterface now support the Messages API, which is fully compatible with LLM API providers such as Hugging Face Text Generation Inference, OpenAI's chat completions API, and Llama.cpp server.

Building Gradio applications around these LLM solutions is now even easier!

gr.Chatbot and gr.ChatInterface now have a type parameter that can accept two values - 'tuples' and 'messages'. If set to 'tuples', the default chatbot data format is expected. If set to 'messages', a list of dictionaries with content and role keys is expected. See below -

def chat_greeter(msg, history):
    history.append({"role": "assistant", "content": "Hello!"})
    return history

Additionally, gradio now exposes a gr.ChatMessage dataclass you can use for IDE type hints and auto completion.

Tool use in Chatbot 🛠️

The Gradio Chatbot can now natively display tool usage and intermediate thoughts common in Agent and chain-of-thought workflows!

If you are using the new "messages" format, simply add a metadata key with a dictionary containing a title key and value. This will display the assistant message in an expandable message box to show the result of a tool or intermediate step.

import gradio as gr
from gradio import ChatMessage
import time

def generate_response(history):
history.append(ChatMessage(role="user", content="What is the weather in San Francisco right now?"))
yield history
time.sleep(0.25)
history.append(ChatMessage(role="assistant",
content="In order to find the current weather in San Francisco, I will need to use my weather tool.")
)
yield history
time.sleep(0.25)

</tr></table>

... (truncated)

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dependabot[bot] commented 4 months ago

Superseded by #212.